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# Senior Staff Machine Learning Engineer

- **Company:** Meta
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Abstraction Layers, Artificial Intelligence, Architectural Patterns, Software Debugging, Machine Learning, Object Detection, Azure Machine Learning, Feature Store, Large Language Models, Prompt Engineering, Data Analytics, Machine Learning Operations
- **Published:** October 23, 2026
- **Apply:** https://dejobs.org/x/x/D6BBCC0C8ABF4C568B644275FC4526AE/job/

## About the Role

11. 12+ years of experience designing, building, and operating large-scale machine learning systems in production environments
 12. Experience architecting ML infrastructure components such as distributed training pipelines, model serving systems, feature stores, or ML evaluation frameworks at a significant scale
 13. Experience in identifying and resolving systemic performance, reliability, or correctness issues that span multiple ML systems or abstraction layers
 14. Experience influencing technical direction across multiple engineering teams through written proposals, design reviews, and cross-functional stakeholder alignment
 15. Experience defining engineering standards, architectural patterns, or platform capabilities that have been adopted broadly across an organization

Preferred Qualifications:

Preferred Qualifications:

 16. Experience in building large-scale object recognition models
 17. Experience collaborating with applied research teams to translate theoretical ML advances into production-grade system improvements
 18. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
 19. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
 20. Experience developing novel approaches to ML observability, experiment management, or model lifecycle tooling that improved engineering velocity across teams
 21. Experience leading multi-year ML platform or infrastructure migrations in complex, mature technical environments
 22. Experience in training large language models to production systems at scale
 23. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

## Description

1. Identify and solve the most complex machine learning systems problems across the organization, including model training efficiency, large-scale inference optimisation, and cross-system ML pipeline reliability
 2. Define and own the technical architecture for critical ML infrastructure components, ensuring designs are extensible, maintainable, and capable of supporting the next generation of model development at Meta's scale
 3. Establish engineering standards and invariants for ML systems that prevent entire categories of reliability, correctness, and performance issues across multiple teams
 4. Drive cross-organizational technical strategy by assessing emerging ML technologies, evaluating trade-offs between competing approaches, and influencing roadmap decisions with leadership and cross-functional partners
 5. Lead the design and delivery of org-wide ML platform initiatives, coordinating across engineering teams, research, product, and data science to align on priorities and drive execution through ambiguity
 6. Develop new metrics and data-driven frameworks for evaluating ML system health, model quality, and infrastructure efficiency, and connecting them to organization-level goals
 7. Mentor and coach engineers across the organization on ML system design, debugging complex models and infrastructure failures, and developing scalable solutions to recurring technical challenges
 8. Collaborate with privacy, security, and compliance teams to design ML systems that uphold user safety, meet regulatory requirements, and set the standard for responsible AI practices within the business domain
 9. Identify and eliminate engineering bottlenecks through improved tooling, automation, and process improvements that make ML development faster and more reliable across the organization
 10. Contribute to recruiting efforts by building relationships with candidates who bring critical ML systems expertise, and serve as a technical reference point for evaluating engineering talent at the most senior levels